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Applied and Industrial Microbiology
Research Article
24 November 2021

Candidatus Kaistella beijingensis sp. nov., Isolated from a Municipal Wastewater Treatment Plant, Is Involved in Sludge Foaming

ABSTRACT

Biological foaming (or biofoaming) is a frequently occurring problem in wastewater treatment plants (WWTPs) and is attributed to the overwhelming growth of filamentous bulking and foaming bacteria (BFB). Biological foaming has been intensively investigated, with BFB like Microthrix and Skermania having been identified from WWTPs and implicated in foaming. Nevertheless, studies are still needed to improve our understanding of the microbial diversity of WWTP biofoams and how microbial activities contribute to foaming. In this study, sludge foaming at the Qinghe WWTP of China was monitored, and sludge foams were investigated using culture-dependent and culture-independent microbiological methods. The foam microbiomes exhibited high abundances of Skermania, Mycobacterium, Flavobacteriales, and Kaistella. A previously unknown bacterium, Candidatus Kaistella beijingensis, was cultivated from foams, its genome was sequenced, and it was phenotypically characterized. Ca. K. beijingensis exhibits hydrophobic cell surfaces, produces extracellular polymeric substances (EPS), and metabolizes lipids. Ca. K. beijingensis abundances were proportional to EPS levels in foams. Several proteins encoded by the Ca. K. beijingensis genome were identified from EPS that was extracted from sludge foams. Ca. K. beijingensis populations accounted for 4 to 6% of the total bacterial populations in sludge foam samples within the Qinghe WWTP, although their abundances were higher in spring than in other seasons. Cooccurrence analysis indicated that Ca. K. beijingensis was not a core node among the WWTP community network, but its abundances were negatively correlated with those of the well-studied BFB Skermania piniformis among cross-season Qinghe WWTP communities.
IMPORTANCE Biological foaming, also known as scumming, is a sludge separation problem that has become the subject of major concern for long-term stable activated sludge operation in decades. Biological foaming was considered induced by foaming bacteria. However, the occurrence and deterioration of foaming in many WWTPs are still not completely understood. Cultivation and characterization of the enriched bacteria in foaming are critical to understand their genetic, physiological, phylogenetic, and ecological traits, as well as to improve the understanding of their relationships with foaming and performance of WWTPs.

INTRODUCTION

Biological foaming in wastewater treatment plants (WWTPs) has been a major concern for activated sludge processes for decades (13) and occurs globally (47). The gray to cream-colored foam is very thick, viscous, stable, and rich in filamentous bacterial biomass (8, 9). The foam results in additional operational difficulties (10, 11) and has raised serious concerns regarding the spread of pathogens within aerosols, thereby posing potential public health risks (12, 13).
Both biological and abiological factors contribute to sludge foaming in WWTPs (10). Abiological factors involved in WWTP foam accumulation include operating temperatures and lipid loading (14). In addition, it is well accepted that stable biological foams are generated from the selective enrichment of hydrophobic bacteria, and especially filamentous hydrophobic bacteria (2, 15, 16). Microscopic examinations have revealed that foams contain high numbers of Gram-positive branched or nonbranched long filamentous bacteria (17, 18). Furthermore, microbiological studies using cultivation-based or molecular biology-based tools have shown that mycolic acid-containing actinomycetes (19) (also termed mycolata) including members of the genera Gordonia, Skermania, Mycobacterium, and Rhodococcus are frequently identified in sludge foams (15, 20). In addition, comprehensive microbial diversity studies of foam and foaming sludge have revealed that non-commonly identified bacterial taxa are also involved in sludge foam formation (12).
Despite these efforts, the occurrence, deterioration, and disappearance of foams in many WWTPs are still not completely understood. Previously unknown foaming bacteria might play a role via their associations with foam formation and/or interactions with environmental factors and other bulking and foaming bacteria (BFB) in sludge. In this study, seasonal foaming was monitored in a municipal WWTP of Qinghe county, Beijing (here referred to as the Qinghe WWTP), for 2 years in order to investigate foam and activated sludge microbiomes. DNA sequencing and bacterial cultivation investigations revealed unique microbial characteristics of foaming sludge in addition to the identification of a foam-enriched, potentially novel foaming bacterium, Candidatus Kaistella beijingensis. The Ca. K. beijingensis genome was sequenced, and the isolate was physiologically and biochemically characterized in addition to investigating its involvement in sludge foaming. Our results demonstrated that Ca. K. beijingensis was seasonally dynamic and was not a core node among the WWTP community network, but its abundances were negatively correlated with the well-studied BFB Skermania piniformis among cross-season Qinghe WWTP communities.

RESULTS AND DISCUSSION

Sludge foaming at the Qinghe WWTP.

The Qinghe WWTP is one of the major processing facilities for sewage (∼400,000 m3/day) from downtown Beijing. Seasonal sludge bulking has occurred in recent years due to the extensive growth of Candidatus Microthrix parvicella (21, 22). However, sludge bulking was successfully controlled when sludge loading rates were increased to >0.14 kg chemical oxygen demand (COD) (kg mixed liquor suspended solids [MLSS] day)−1 (23). The sludge load was further controlled above 0.1 kg COD (kg MLSS day)−1 in the Qinghe WWTP. In March 2017, the biochemical tank of the Qinghe WWTP was modified from the inverted anaerobic/anoxic/oxic (A2O) process to the anoxic/oxic (A/O) process as Fig. S1 in the supplemental material shows. In order to improve the denitrification efficiency under low temperatures, a 300% volume of nitrification liquids was returned to the influent wastewater, and sodium acetate was added to the anoxic tank. After upgrading the process, considerable biological foaming has occurred, and a clear relationship of sludge foaming and sludge volume index (SVI) values was not observed (Fig. 1a). The Qinghe WWTP operation during 2017 to 2018 was monitored for temperature (T) and dissolved oxygen (DO) in addition to influent COD, total nitrogen (TN), and total phosphorus (TP) concentrations, sludge retention time (SRT), hydraulic retention time (HRT), pH, SVI values, and sludge loading rates. These data are provided in Data Set S1.
FIG 1
FIG 1 Qinghe WWTP operation during 2017 to 2018 showing sludge foaming and bacteria enriched in biological foams based on culture-dependent and -independent methods. (a) Sludge foaming photos and sludge volume index (SVI) values. Additional data are available in Data Set S1. (b) Bacterial taxa (abundances of >1%) enriched in biological foams or activated sludge based on shotgun metagenomic analyses. The diameters of nodes indicate the abundances of taxa, while the color represents the log2 ratio of median abundances compared between the activated sludge and foaming samples. Darker red nodes indicate higher abundances in biological foams. (c) Phylogenetic tree showing all representative strains isolated from biological foams. The isolate number and 16S rRNA gene similarities against type strains are shown on the tree.

Sludge foams harbor unique microbiomes.

A total of 170 samples were taken from the anoxic/oxic tank of the Qinghe WWTP from March 2017 to December 2018, of which 143 were from activated sludges and 27 were from foams. Of the 170 samples, 48 (36 sludge and 12 foam samples, as indicated in Data Set S2) were subjected to shotgun metagenomic sequencing, and each sample produced an average of 199,818,083 clean reads of 150 bp in length. Significantly different (P < 0.05) sequencing depths were not observed between activated sludge and foam data sets (Data Set S2). Classification with Kraken 2 indicated that approximately 40% of clean reads from the activated sludge and 55% of clean reads from foam were assigned into archaeal or bacterial lineages.
Archaea accounted for only approximately 0.6% and 0.3% of the classified clean reads in activated sludge and foams, respectively. Consequently, we focused our analyses on the bacterial communities. The largest difference between the foam and the sludge communities at the phylum level was the presence of much higher abundances of Actinobacteria in the foam (Fig. 1b). Actinobacteria accounted for 35.14% of bacterial abundances in the foam and were 1.76-fold higher than in the sludge (Wilcox t test, Padjusted [Padj] < 0.001). Among the Actinobacteria, the two mycolata genera Skermania and Mycobacterium were present at relatively high abundances within the foams. Skermania piniformis accounted for 24.69% of bacterial communities with 46.35-fold-higher abundances in foams than in sludges (Wilcox t test, Padj < 0.001). Mycobacterium accounted for 2.51% of bacterial abundances, with 1.66-fold-higher abundances in foams than in sludges (Wilcox t test, Padj < 0.05). Previously discovered (12) foam-forming mycolata genera including Gordonia, Nocardia, and Rhodococcus were not dominant (maximum bacterial abundance of all samples < 1%) in foams, nor were their abundances significantly different between foam and sludge samples. Although the abundances of the class Bacteroidia were not significantly different between sludge and foam samples, the Flavobacteriales order of the Bacteroidia lineage accounted for an average 4.82% of bacterial abundances and was 3.55-fold higher in foams than in sludges (Wilcox t test, Padj < 0.001). Two genera from the Flavobacteriales including Kaistella (average bacterial abundance of 2.93% and 34.26-fold-higher abundances in foams than in sludges) and Flavobacterium (average 0.68% of bacterial abundances and 2.73-fold-higher abundances in foams than in sludges) were present at relatively high abundances in foams. Among the Burkholderiaceae family of the Proteobacteria phylum, Acidovorax_B, Acidovorax_D, Comamonas_D, Giesbergeria, Hydrogenophaga, and Rhodoferax were significantly enriched in foams, with average abundances of 1.10%, 10.90%, 0.77%, 6.63%, 1.02%, and 0.66% in foams, respectively. Novosphingobium_sp002440635 from the Sphingomonadales was also enriched in foams with an average 0.40% bacterial abundance and 11.99-fold-higher values in foams than in sludges. Among the Proteobacteria phylum, Acidovorax_A, Acidovorax_C, Limnohabitans of the Burkholderiaceae group, and Acinetobacter of the Moraxellaceae group were enriched in the foams. Likewise, the Saprospiraceae of the Bacteroidota lineage were also enriched in foams, although their abundances were lower than 1% in all samples. These results indicated that many previously unknown bacterial taxa within the Proteobacteria and Bacteroidota might also act as foaming bacterial agents and play important roles in foaming occurrence and deterioration. As the foaming severities and SVI values were similar in March 2017 and March 2018, we did not find significant changes of taxon abundances in foaming communities. The result indicated that the foaming communities were similar in the annual bacterial successions of the Qinghe WWTP.

Cultivation of the foam-enriched species Candidatus Kaistella beijingensis.

In addition to metagenomic analysis, intensive isolation and cultivation of microbial populations were conducted from the Qinghe WWTP samples. A total of 228 isolates were obtained from the foam samples. The isolates were identified by sequencing almost complete 16S rRNA genes and were subsequently phylogenetically attributed to 61 potential species (based on 16S rRNA gene identities of >98% within taxa) from the Actinobacteria, Bacteroidetes, Deinococcus-Thermus, Firmicutes, and Proteobacteria groups. Sixty-one representative strains and their closest species-level classifications are shown in Fig. 1c. Strain PM-38 was repeatedly isolated and cultivated (18 isolates) and is closely related to Kaistella sp002426785, which was identified from a metagenome (Data Set S3). Strain PM-38 exhibited 16S rRNA gene sequence identities of 95.89%, 95.87%, and 95.55% to Kaistella haifense H38T, Kaistella anthropi NF 1366T, and Kaistella treverense IMMIB L-1519T, respectively. Thus, strain PM-38 represented a novel species within the genus Kaistella. We consequently propose the taxonomic epithet of Candidatus Kaistella beijingensis. The genome sequence of strain PM-38 was included in an expanded Kraken 2 database that was used for subsequent analyses.

Morphological, genomic, and phylogenetic features of Ca. K. beijingensis.

The morphology of strain PM-38 is shown in Fig. 2a and b and Fig. S2. Cells were Gram stain negative, non-spore-forming, rod shaped, 0.3 to 0.35 μm wide, and 1.5 to 2.5 μm long with peritrichous fimbriae (Fig. 2a). Long rods (10 to 20 μm) of cells were observed (Fig. 2b). Cells were nonmotile without flagella. Strain PM-38 did not grow under anaerobic conditions, although it remained alive under anaerobic conditions for a week. Colonies of strain PM-38 on R2A agar were circular, convex, and smooth, in addition to being cream yellow and semitransparent, with wet surfaces. Flexirubin-type pigments were not produced. Colony diameters were about 2 mm after 2 days of incubation on R2A agar at 30°C.
FIG 2
FIG 2 Morphology and genome of Ca. K. beijingensis strain PM-38. (a and b) Images showing the cellular morphology of strain PM-38 based on scanning electron microscopy. Panel a shows a magnification of the single cell that is marked in panel b. (c) A circular map of the genomic chromosome of strain PM-38. From the outside to the center, circle 1 shows the gene GC percent deviation (gene GC% − genomic mean GC%), circle 2 shows predicted CDSs transcribed in the clockwise direction, circle 3 shows predicted CDSs transcribed in the counterclockwise direction, circle 4 shows gene GC skew, and circle 5 shows rRNA (blue), tRNA (green), miscellaneous RNA (orange), transposable elements (chocolate brown), and pseudogenes (yellow). (d) Gene numbers of strain PM-38 associated with general COG functional categories. Functional categories include cell cycle control, cell division, and chromosome partitioning (D); cell wall/membrane/envelope biogenesis (M); cell motility (N); posttranslational modification, protein turnover, and chaperones (O); signal transduction mechanisms (T); intracellular trafficking, secretion, and vesicular transport (U); defense mechanisms (V); extracellular structures (W); translation, ribosomal structure, and biogenesis (J); transcription (K); replication, recombination, and repair (L); energy production and conversion (C); amino acid transport and metabolism (E); nucleotide transport and metabolism (F); carbohydrate transport and metabolism (G); coenzyme transport and metabolism (H); lipid transport and metabolism (I); inorganic ion transport and metabolism (P); secondary metabolite biosynthesis, transport, and catabolism (Q); general function prediction only (R); and function unknown (S).
The complete genome of strain PM-38 comprised a single circular chromosome of 2,816,191 bp with a GC content of 36.78% (Fig. 2c). No plasmids were detected. A total of 2,786 genomic objects were identified including 2,708 coding gene sequences (CDSs), 38 tRNA replicons, six rRNA replicons, 25 miscellaneous RNA replicons, and one transfer-mRNA (tmRNA) replicon. Two copies of 16S rRNA genes were present in the genome, and they exhibited one base pair difference (C/T). Of the 2,708 CDSs, 1,889 were classified within at least one Clusters of Orthologous Groups (COG) category, with 516 CDSs annotated as within the cellular processes and signaling category, 460 within information storage and processing, 802 within metabolism, and 447 that were poorly characterized (Fig. 2d).
Strain PM-38 was phylogenetically closely related to members of the genera Kaistella, Epilithonimonas, and Chryseobacterium. Phylogenetic trees reconstructed with neighbor-joining, maximum-likelihood (ML), and maximum-parsimony (MP) methods also indicated that strain PM-38 comprised a distinct clade with members of the genus Kaistella that was well separated from other genera of the family Weeksellaceae including Epilithonimonas and Chryseobacterium (Fig. S3). The estimated DNA-DNA hybridization (DDH) values between strain PM-38 and other Kaistella species were lower than 21.70%, which was much lower than the standard threshold value (70%) for interspecific differentiation. Thus, strain PM-38 represents a novel species of the genus Kaistella.

Physiology, metabolism, and growth of Ca. K. beijingensis.

To explore the physiology of strain PM-38, metabolic pathways and cellular transport systems were reconstructed based on genome annotations (Fig. 3a; see also Data Set S4). Strain PM-38 possessed a complete glycolysis/gluconeogenesis pathway and a pentose phosphate pathway for carbon substrate assimilation and energy conservation. Phenotypic tests confirmed that strain PM-38 utilized glycogen, d-glucose, maltose, propionate, valeric acid, and acetate. Strain PM-38 exhibited the ability to produce many hydrolases to degrade complex substrates like glycogen, starch, esculin, gelatin, and casein. Genomic analysis suggested that PM-38 exhibited the potential to degrade pectins, which are complex polysaccharides that are rich in galacturonic residues (24) and might be responsible for the viscosity and volume of biological foam. In addition, strain PM-38 cells were very hydrophobic, similar to known mycolata like Rhodococcus ruber and Rhodococcus erythropolis (Fig. 3e). Previous studies have shown that S. piniformis is known to be highly hydrophobic in pure culture, and cells have been reported to adhere preferentially to hydrophobic substrates (25). Filaments of S. piniformis appeared to be comparatively hydrophobic in situ, far beyond other cells, using the assay for microsphere adhesion to cells (2628). We obtained S. piniformis DSM 43998T from DSMZ-German Collection of Microorganisms and Cell Cultures. In our study, S. piniformis was strongly hydrophobic, floated on top, and could not mix with liquid medium. Although our quantitative method used for cellular hydrophobicity was not applied to S. piniformis, we can speculate that the cellular hydrophobicity of S. piniformis was much higher than that of other strains we tested. It has been proposed that stable foams are generated by selective enrichment of hydrophobic bacteria in a WWTP due to flotation (1, 2), while the hydrophobic bacterial cells are also responsible for long-term foam stabilization.
FIG 3
FIG 3 Physiology, metabolism, and growth characteristics of Ca. K. beijingensis PM-38. (a) Predicted physiology and metabolic reconstruction of Ca. K. beijingensis strain PM-38 based on genomic annotations (see Data Set S4 in the supplemental material). P, phosphate; PRPP, 5-phospho-alpha-d-ribose-1-diphosphate; CoA, coenzyme A; TCA, tricarboxylic acid. (b and c) The growth (b) and dynamics of protein concentrations in the supernatant (c) of Ca. K. beijingensis PM-38 cultures in R2A broth grown at 30°C compared to Escherichia coli Trans1, Comamonas testosteroni CNB1, Casimicrobium huifangae SJ-1, Rhodococcus erythropolis Q1R30-27, and Rhodococcus ruber H1Y01 cultures. (d) Growth characteristic of Ca. K. beijingensis PM-38 in R2A broth incubated at different temperatures. (e) The cell surface hydrophobicities of Ca. K. beijingensis PM-38 compared with core activated sludge taxa and known BFB. (f and g) EPS proteins secreted by Ca. K. beijingensis PM-38 visualized by differences and dispersion (f) or effect size and Welch’s t test (g) between the activated sludge and biological foaming groups. Red dots indicate proteins enriched in biological foaming, blue dots indicate proteins enriched in activated sludge, and gray dots are rare and not significantly different, while black dots are abundant but not significantly different.
The genome of strain PM-38 encoded a large number of proteases and peptidases that would enable PM-38 to degrade proteinaceous substances in sewage or foams. Phenotypic tests also confirmed that PM-38 produced multiple esterases and lipases. The encoding of a complete β-oxidation pathway in strain PM-38 (Fig. 3a) likely allows the degradation of fatty acids. Spermidine, putrescine, phospholipids, and cholesterol are abundant in municipal wastewaters (29). Consequently, strain PM-38 might function in the removal of spermidine, putrescine, phospholipids, and cholesterol from WWTPs, as suggested by the presence of genes annotated as encoding transporters for these substances. Interestingly, the strain PM-38 genome also contains genes for the biosynthesis of glycogen (Fig. 3a), suggesting that glycogen might be used for storage when carbon sources are in excess or when cells are experiencing high C/N ratios. Strain PM-38 grew over a temperature range of 15 to 37°C, and optimal growth occurred at 30°C (Fig. 3d).

Production and detection of extracellular proteins from Ca. K. beijingensis in pure culture, activated sludges, and foams.

The genome of Ca. K. beijingensis strain PM-38 encoded synthesis and secretion proteins to produce extracellular polymeric substances (EPS) like polysaccharides, lipopolysaccharides, lipoproteins, and lipids (Fig. 3a). EPS plays important roles in the formation and stabilization of biological foams (30, 31). The growth and production of extracellular proteins by PM-38 were monitored and compared against the same activities of core activated sludge bacteria including Casimicrobium huifangae (29) and Comamonas testosteroni (32) in addition to the well-known mycolata R. ruber and R. erythropolis, which were reported involved in the foaming formation (2) (Fig. 3b and c). Strain PM-38 produced much higher levels of proteins than C. huifangae, C. testosteroni, R. ruber, and R. erythropolis (Fig. 3c).
Protein compositions of EPS from activated sludge and foams were analyzed. A total of 19 proteins from Ca. K. beijingensis PM-38 in sludge and foaming samples were predicted using the protein database from the genome of strain PM-38 (Fig. 3f; see also Data Set S5). Seven of the 19 were enriched in foam communities (Fig. 3g). Four proteins (tag no. 0522, 0580, 1131, and 1803 within the genome annotations) were annotated as conserved exported proteins of unknown function. One protein identified as no. 0095 was annotated as a fasciclin that might be involved in cell adhesion (33). Two proteins (tag no. 1904 and 2597) were annotated as being within the G-D-S-L family of lipolytic proteins involved in lipid degradation and an 1,4-alpha-glucan branching enzyme involved in EPS modification, respectively. Previous studies have reported that long-chain fatty acids were more abundant in biological foams and could stimulate the growth of some BFB (15, 34). The enriched lipolytic proteins in foam EPS could support previous empirical conclusions to some extent. In general, these secreted proteins enriched in foams were hypothesized to help Ca. K. beijingensis PM-38 be more competitive in foaming microbiomes.

The occurrence and distribution of Ca. K. beijingensis are correlated with EPS levels.

Ca. K. beijingensis was detected in both activated sludges and foams during 2017 to 2018, but its abundances varied seasonally (Fig. 4a to d). Ca. K. beijingensis was enriched in foam communities and accounted for 0.5 to 6% and 9 to 15% of foam microbial populations, according to shotgun metagenomic and 16S rRNA gene data, respectively. At the operational temperatures (16 to 27°C) of the Qinghe WWTP, the abundances of Ca. K. beijingensis in foam samples were significantly and negatively correlated (r = −0.87, P < 0.001) with temperatures (Fig. 4f). However, the abundances of Ca. K. beijingensis were positively related to the contents of proteins, polysaccharides, and free fatty acids in EPS extracted from foam samples (Fig. 4g to i). However, correlations of Ca. K. beijingensis abundances to temperatures and EPS contents (proteins, polysaccharides, and free fatty acids) were not observed among activated sludge communities. Lemmer (9) proposed that hydrophobic granular substances and soluble organic matter content are enriched at the surface of gas bubbles, thereby allowing the generation of foam bubbles. These foam bubbles would accumulate on the surface of waters, ultimately resulting in WWTP foaming. The hydrophobic nature of Ca. K. beijingensis cells would lead to their association with foam bubbles in activated sludge. Furthermore, Ca. K. beijingensis floating on the foam surfaces can utilize the abundant lipids, long-chain fatty acids, and so on. At the same time, it can secrete extracellular polymeric substances. These exported proteins may work as biosurfactants, contributing to the sludge foaming (30, 35). Although higher abundances of Ca. K. beijingensis were found in serious sludge foaming, the causality relationship between Ca. K. beijingensis and sludge foaming needs more exploration. We cannot rule out the possibility that sludge foaming occurs prior to the enrichment of Ca. K. beijingensis due to the complex origin of sludge foaming.
FIG 4
FIG 4 Ca. K. beijingensis populations are correlated with season, temperature, and EPS levels. (a) Dynamics of Ca. K. beijingensis abundances in activated sludge based on shotgun metagenomic data. (b) Dynamics of Ca. K. beijingensis abundances in biological foams based on shotgun metagenomic data. (c) Dynamics of Ca. K. beijingensis abundances in activated sludge represented by 16S rRNA gene V4 tags. (d) Dynamics of Ca. K. beijingensis abundances in biological foams represented by 16S rRNA gene V4 tags. (e) The abundances of Ca. K. beijingensis in activated sludge of other locations estimated by 16S rRNA gene V4 tags. (f) The 16S rRNA gene abundances of Ca. K. beijingensis varied with temperature. (g) The 16S rRNA gene abundances of Ca. K. beijingensis varied with EPS protein concentrations. (h) The 16S rRNA gene abundances of Ca. K. beijingensis varied with polysaccharide contents in EPS. (i) The 16S rRNA gene abundances of Ca. K. beijingensis varied with free fatty acid contents in EPS.
The 16S rRNA gene sequence of Ca. K. beijingensis was compared against the global water microbiome consortia (GWMC) data sets (32) but did not yield close matches. This result could be due to the low abundances of Ca. K. beijingensis in activated sludges. Further comparison to other WWTP communities in Wuxi and Shanghai of China and WWTPs in Denmark (3638) yielded matches to Ca. K. beijingensis, albeit in low community abundances (<0.005%) (Fig. 4e). Furthermore, matches to Ca. K. beijingensis 16S rRNA genes were observed in the WWTP of Tsingtao city, which is similar to the Qinghe WWTP in Beijing (36). Moreover, a not-yet-cultured and unidentified Kaistella species was observed in the GTDB genomic database (no. sp002426785). The uncharacterized Kaistella sp002426785 genome exhibited 97.98% average nucleotide identity (ANI) to the genome of Ca. K. beijingensis PM-38 and was associated with two metagenomic bins, UBA5538 (GCA_002426785) and UBA8538 (GCA_003536155). UBA5538 was generated from the metagenome (SRX472115) of a sequencing batch reactor (SBR) that was treating saline wastewater (1% salinity) in Hong Kong that contained high phosphorus contents (39). UBA5538 was generated from the metagenome (SRX206471) of SBR-enriched microbial communities from a Danish wastewater treatment plant. Thus, the distribution of Ca. K. beijingensis was not limited only to the Qinghe WWTP.

Cooccurrence of Ca. K. beijingensis with other bulking and foaming bacteria (BFB).

In addition to the generation of metagenomes from 48 representative samples, the V4 hypervariable region of 16S rRNA genes from all 170 samples collected from the Qinghe WWTP was sequenced. A total of 168, 97, 162, and 163 OTUs were enriched in foam samples from spring, summer, autumn, and winter, respectively (Fig. 5a to d). A total of 29 operational taxonomic units (OTUs) were significantly enriched among all seasons, and five of the 29 OTUs comprised previously known BFB (Fig. 6), including Skermania piniformis (OTU_3810), type 1863 Acinetobacter sp. (OTU_4162 and OTU_4828), type 1701 Sphaerotilus sp. (OTU_5204), and type 1863 Cloacibacterium (OTU_1430) (11). The other 24 OTUs (Data Set S6) comprised bacterial taxa that were previously unknown to associate with sludge foams, although their contribution to sludge foaming remains unknown. The 16S rRNA gene sequence of the highly abundant and foam-enriched OTU_7791 exhibited a 100% similarity with the 16S rRNA gene of strain PM-38. In addition, the previously identified foaming bacterium Mycobacterium fortuitum (OTU_1602) (40) was significantly enriched in foams in spring to autumn, but not in winter.
FIG 5
FIG 5 Bacteria enriched in biological foam communities and the relationships between Ca. K. beijingensis and other BFB in biological foam communities of the Qinghe WWTP. (a to d) The enriched and depleted OTUs in biological foams across four seasons, as determined by differential abundance analysis. Each point represents an individual OTU, and the position along the x axis represents the abundance fold change compared with activated sludge, while the position along the y axis indicates statistical significance. FDR, false-discovery rate. Red dots indicate OTUs enriched in biological foams, and green dots indicate OTUs depleted in biological foams. Dot sizes indicate the average abundance of OTUs in foams. If OTU abundances are >0.6%, annotations are labeled. (e) Each node represents an OTU in the Qinghe WWTP. A connection indicates a strong (Spearman’s ρ > 0.8) and significant (Padjusted < 0.05) correlation. Node colors indicate different phyla, while node sizes indicate average abundances in biological foam communities. Blue edges indicate positive correlations between nodes, and orange edges indicate negative correlations. Potential foaming bacteria based on our analyses are labeled. (f) Cooccurrence network between Ca. K. beijingensis (OTU_7791) and its adjacent, first-level, and second-level OTUs in the biological foaming communities. The nodes representing OTUs were annotated to their lowest classification level.
FIG 6
FIG 6 The abundances of known bulking and foaming bacterium (BFB) populations (represented by 16S rRNA gene V4 tags) varied in seven sampling times. (a) Dynamics of BFB abundances in activated sludge. (b) Dynamics of BFB abundances in biological foams.
The association networks of foam microbiomes were subsequently explored (Fig. 5e). With the exception of the Simplicispira (OTU_510), Mycobacterium fortuitum (OTU_1602), and type 1863 Acinetobacter sp. (OTU_4162) OTUs, foam-enriched bacteria appeared as peripheral nodes among two “centered” modules in the network (Fig. 5e and Fig. S4a). Taxa in these “centered” modules are universal activated sludge bacteria (Fig. S4b and c). The primary and secondary connections to Ca. K. beijingensis are shown in Fig. 5f. Ca. K. beijingensis did not exhibit any positive connection to other taxonomic nodes at the first linkage level but exhibited significantly negative relationships with four OTUs including OTU_8786 (Nitrosomonadaceae), OTU_1826 (Sphingobacteriales), OTU_7702 (Novosphingobium), and OTU_3810 (filamentous Skermania piniformis) that exhibited the strongest negative correlations. These negative correlations suggested either competition among Ca. K. beijingensis and the BFB populations represented by OTU_3810 (Skermania piniformis), OTU_7702 (Novosphingobium), and OTU_6698 (Emticicia) or, alternatively, differential adaptations to distinct environments. Ca. K. beijingensis was able to grow on a range of organic compounds including sugars and lipids, while other studies (34, 41) have shown that Skermania piniformis and Sphingobacteriales are also robust heterotrophs. Thus, these organisms might grow with coexclusions in foaming environments. We analyzed the metabolic genes of Skermania piniformis DSM 43998T (Data Set S7). S. piniformis DSM 43998T is able to degrade glycerol and triacylglycerol and encodes more phospholipases than Ca. K. beijingensis PM-38. In addition, S. piniformis DSM 43998T encodes many more enzymes involved in the reactions of fatty acid β-oxidation pathway and oleate β-oxidation pathway (Fig. S5 and Data Set S7). These abundant enzymes enable cells to utilize more diverse substrates of fatty acids and other lipid hydrolysates. Previous studies also confirmed that S. piniformis uptakes glycerol trioleate, glycerol, oleic acid, and palmitate, as well as Tween 20, 40, and 60 (34). In addition, S. piniformis can store polyphosphates and polyhydroxyalkanoates in cells, which could help it survive in adverse environments (28). However, the minimum growth temperature for S. piniformis was 15°C (20). Besides, the growth rate was very low (∼21 day for visible colonies) compared with Ca. K. beijingensis. Our results indicated that Ca. K. beijingensis was well adapted to low temperatures (4 to 15°C) and was more abundant in winter and spring WWTP samples than summer and autumn samples (Fig. 4a to d). Ca. K. beijingensis approached similar levels of biomass within 3 days when cultured at 15°C as it did at 30°C (Fig. 3d) (27).

Conclusions.

Sludge foaming at the Qinghe WWTP was monitored and investigated using culture-dependent and culture-independent microbiological methods. Foam microbiomes were characterized and exhibited high abundances of Skermania, Mycobacterium, Flavobacteriales, and Kaistella. A previously uncharacterized bacterium, Candidatus Kaistella beijingensis, was successfully cultivated, its genome was sequenced, and it was phenotypically characterized. Ca. K. beijingensis exhibits a hydrophobic cell surface, EPS production, and lipid metabolism. Furthermore, the abundances of Ca. K. beijingensis were proportional to EPS levels in foams. Several proteins encoded by the Ca. K. beijingensis genome were identified from EPS extracted from the foams. Ca. K. beijingensis exhibited seasonal dynamics in the Qinghe WWTP, and its abundances were highest in spring. Cooccurrence analysis indicated that Ca. K. beijingensis was not a core node in the WWTP’s bacterial network, and its abundances were negatively correlated with those of the previously well-known BFB, Skermania piniformis.

Taxonomy.

Description of Candidatus Kaistella beijingensis sp. nov. (bei.jing.ensis. N.L. fem. adj. beijingensis, of or pertaining to Beijing, the capital of People’s Republic of China, where the type strain was isolated). Displays the following properties: colonies are approximately 2.0 to 3.0 mm in width, cream yellow, and semitransparent in color, with regular circular margins and a wet convex appearance after 3 days of incubation at 30°C. Most cells are 0.3 to 0.35 μm in width and 1.5 to 2.5 μm in length. Strain PM-38 grew at 4 to 37°C, with optimum growth at 30°C in R2A medium. Strain PM-38 can tolerate pH from 5.5 to 9.5 (optimum 7.5) and NaCl concentrations (wt/vol) from 0 to 2.5% (optimum 0%). According to the API ZYM, 20NE, ID 32GN (bioMérieux), Biolog GEN III systems, and genome analysis, strain PM-38 hydrolyzed esculin, gelatin, casein, and Tween 60 while weakly hydrolyzing starch. The strain did not hydrolyze tyrosine, cellulose, urea, Tween 20, Tween 40, or Tween 80. Cellular enzyme activity tests were positive for catalase, alkaline phosphatase, acid phosphatase, esterase (C4), esterase lipase (C8), lipase (C14), leucine arylamidase, valine arylamidase, cystine arylamidase, naphthol-AS-BI-phosphohydrolase, trypsin, α-glucosidase, and α-chymotrypsin assays but negative for oxidase, α-galactosidase, β-galactosidase, β-glucuronidase, β-glucosidase, N-acetyl-β-glucosaminidase, α-mannosidase, α-fucosidase, and arginine dihydrolase. Strain PM-38 utilizes glycogen, d-glucose, maltose, propionate, valeric acid, and acetate while weakly utilizing lactate. Strain PM-38 resisted cefixime antibiotics (5 μg) with no inhibition zone observed. The isolate cannot reduce nitrate to nitrite or reduce nitrite further to ammonia or gas (N2, NO, and N2O). The genomic GC content of PM-38 is 36.78%. Menaquinone MK-6 is the major isoprenoid quinone. The major cellular fatty acids (only values of ≥5% are reported) are 15:0 iso (16.71%), 15:0 anteiso (16.48%), 16:0 iso 3-OH (12.75%), summed feature 3 (10.86%; comprising 16:1 ω6c and/or 16:1 ω7c), 14:0 iso (10.15%), 16:0 iso (8.98%), and 17:0 iso 3-OH (6.25%) after strain PM-38 grew at 30°C on R2A medium for 3 days. The detailed comparisons of cellular fatty acids among Kaistella species cultured on LB medium are shown in Table S1. The type strain, PM-38 (= CGMCC 1.30062 = NBRC 114000), was isolated from biological foams within the Qinghe WWTP, Beijing, China.

MATERIALS AND METHODS

Monitoring of WWTP operation and sampling of sludge and foams.

The Qinghe WWTP is located at 116°21′27.25′′E, 40°02′35.54′′N, and is one of the major facilities that processes sewage (∼400,000 m3/day) from downtown Beijing. WWTP performance was routinely monitored for operational parameters including sludge volume index (SVI), sludge retention time (SRT), hydraulic retention time (HRT), and sludge loading, in addition to influent and effluent concentrations of COD, nitrogen, and phosphorus (shown in Data Set S1 in the supplemental material). Sampling was conducted seasonally for 2 years (from spring of 2017 to winter of 2018) (Fig. 1a). A total of 170 sludge and foam samples were obtained from the anoxic/oxic (A/O) process tank of the WWTP, encompassing different zones of the A/O tank (inlet, anoxic, aerobic, and outlet zones). Temperature, pH, and dissolved oxygen were measured on site when sampling. All samples were collected in triplicate with sterilized 50-ml centrifuge tubes and placed in a portable icebox before being quickly transported to the Environmental Microbiology Research Center (EMRC) at the Institute of Microbiology of Chinese Academy of Sciences. Samples were analyzed for each zone of the A/O tank, and each sample was divided into two portions, with one stored at 4°C for bacterial isolation and cultivation and the other stored at −80°C for subsequent DNA extraction.

Bacterial isolation and cultivation.

Foaming samples from the periods with the most foaming activity in March 2017 and December 2018 were used for bacterial isolation and cultivation. Foam samples (1 ml) were suspended in 9 ml of phosphate-buffered saline (PBS) buffer solution and shaken for 30 min at room temperature to thoroughly mix. Then, 200 μl of 10-fold serial dilutions with appropriate cells was spread on R2A plates (42) or R2A medium supplemented with 1% Tween 80. The plates were incubated at 15°C or 30°C under aerobic conditions. Single colonies on the plates were continuously picked and transferred to new R2A or R2A plus Tween 80 agar plates, for 2 weeks. The colonies were repeatedly streaked to confirm purity. Nearly full-length 16S rRNA genes of isolates were amplified using the 27F and 1492R primers (43). The amplified 16S rRNA genes were analyzed using the EzBioCloud database (44). All isolates were clustered based on 16S rRNA gene similarities at the 98% similarity threshold value using the cluster_fast command in USEARCH v11 (45). The 16S rRNA genes of representative isolates from clusters were then aligned with Clustal W (46) to construct a neighbor-joining (47) phylogenetic tree using Kimura’s two-parameter model (48) with 1,000 bootstrap replicates in the MEGA 7 (49) software package, which was then visualized with the ggtree (50) package for R.

Genomic DNA extraction, sequencing, genome assembly, annotation, and phylogenetic analysis.

Ca. K. beijingensis genomic DNA was extracted using the Wizard Genomic DNA purification kit (Promega, USA) following the manufacturer’s instructions. DNA quantity and quality were determined with a NanoVue Plus spectrophotometer (GE Healthcare, USA). Genome sequencing was then performed using both the Illumina HiSeq 4000 and PacBio RS II platforms at BGI (Shenzhen, China). Illumina libraries utilized insert sizes of 270 bp with a paired-end sequencing length of 150 bp. After filtering and removing the adapters, a total of 1,095 Mbp (390× coverage) clean data were recovered from Illumina sequencing. For PacBio sequencing, subreads (length < 1 kbp) were removed with the program Pbdagcon (https://github.com/PacificBiosciences/pbdagcon) for self-correction. A total of 1,382 Mbp (491× coverage) of reliably corrected reads from the PacBio data set were assembled with the Celera Assembler (https://sourceforge.net/projects/wgs-assembler/files/wgs-assembler/wgs-8.3/). Subsequently, the GATK (https://software.broadinstitute.org/gatk/) and SOAP2 (51) software programs were used for single-base corrections using the Illumina clean data to improve the accuracy of assemblies. Finally, a complete circular genome of Ca. K. beijingensis strain PM-38 was recovered with a length of 2,816,191 bp. In addition, we resequenced and assembled the complete 4,230,116-bp genome of filamentous Skermania piniformis DSM 43998T using the same sequencing strategy.
Genomic analysis of Ca. K. beijingensis strain PM-38 was performed using the MicroScope annotation pipeline (52). Gene prediction was conducted with Prodigal (53), and annotations were automatically curated using BLASTP (54) by searching against the Clusters of Orthologous Groups (COG) (55) and the EggNOG v5.0 (56) protein orthologous group databases. Enzymatic classifications were based on PRIAM results (57). Metabolic pathways of Ca. K. beijingensis strain PM-38 were evaluated according to the Kyoto Encyclopedia of Genes and Genomes (KEGG) database (58) using GhostKOALA tools (59). Identification of closely related phylogenetic relatives and calculations of 16S rRNA gene sequence similarities were conducted using the EzTaxon (60) server in addition to nucleotide BLASTs against the NCBI database. The 16S rRNA gene sequence of Ca. K. beijingensis strain PM-38 and related type strain sequences were aligned using the Clustal W (46) software program. Phylogenetic analyses were conducted using the software package MEGA 7 (49) with the neighbor-joining (47), maximum-likelihood (ML) (61), and maximum-parsimony (MP) (62) algorithms. The whole-genome average nucleotide identities (ANIs) and digital DNA-DNA hybridization (dDDH) values for Kaistella genus genomes were calculated using the Orthologous Average Nucleotide Identity Tool (OAT) (63) and the Genome-to-Genome Distance Calculator (64).

Phenotypic characterization of Ca. K. beijingensis.

To evaluate the growth of strain PM-38 on different media, it was cultured on R2A agar (Difco), Trypticase soy agar (TSA; Difco), nutrient agar (NA; Difco), and LB agar (Difco) at 30°C. Subsequently, Ca. K. beijingensis strain PM-38 was routinely cultivated on R2A medium at 30°C for phenotypic and biochemical tests. Cellular morphology was observed with scanning electron microscopy (Quanta 200; FEI) and transmission electron microscopy (JEM-1400; JEOL). Motility was examined with light microscopy. Tests for Gram staining, oxidase, and catalase in addition to hydrolysis of casein, cellulose, starch, tyrosine, Tween 20, Tween 40, Tween 60, and Tween 80 were conducted using universal methods (65). The growth temperature of strain PM-38 was examined at 4°C, 15°C, 25°C, 30°C, 37°C, 41°C, and 45°C in R2A broth (pH 7.2). Growth at pH values of 4.0, 5.0, 6.0, 6.5, 7.0, 7.5, 8.0, 8.5, 9.0, and 10.0 was evaluated by adjusting the pH of R2A broth with 0.2 M sodium acetate buffer (pH 4.0 to 6.5), 0.2 M phosphate buffer (pH 7.0 to 8.0), and 0.2 M sodium carbonate buffer (pH 8.5 to 10.0). The NaCl tolerance of the strain was evaluated in R2A broth supplemented with 0 to 5.0% (wt/vol) NaCl at intervals of 0.5%. Cell growth was estimated by measuring turbidity at 600 nm (OD600) using a UV-visible spectrophotometer (Specord 205; Analytic Jena). Strict anaerobic growth was tested in an anaerobic tube with R2A broth supplemented with 0.5 g liter−1 l-cystine and 1 mg liter−1 resazurin as the reductant and redox indicator, respectively, in addition to the replacement of the upper air layer by nitrogen gas. Other enzyme activities and biochemical characteristics were determined using the commercial systems API ZYM, API 20NE, ID 32GN (bioMérieux), and Biolog GEN III systems according to the manufacturer’s instructions. The presence of flexirubin-type pigments was determined by observing color changes after adding aqueous 20% KOH (66). Antibiotic resistance tests were conducted using the agar diffusion method with antibiotic-impregnated discs (Beijing SanYao Science & Technology Development Co.). Cellular polyphosphate granules were investigated using the Neisser (17), Albert, and 4′,6-diamidino2-phenylindole (DAPI) (67) stains. Cellular fatty acids were extracted according to the standard MIDI (Sherlock Microbial Identification System, version 6.0) protocols, and identification was performed by GC 6890 (Agilent) (68). For comparing cellular fatty acids among type species of Kaistella, cell biomass of PM-38 and the reference strains was harvested from cultures after 3-day growth on LB agar at 30°C. Isoprenoid quinones were then extracted with chloroform-methanol (2:1, vol/vol) and purified by thin-layer chromatography and identified using reversed-phase high-performance liquid chromatography (HPLC) (69).

Cellular hydrophobicity and extracellular protein production.

Cell surface hydrophobicities were determined using microbial adherence to hydrocarbon (MATH) assays with n-hexadecane as the solvent (2, 70). Extracellular proteins were then determined after cultures were filtered using 0.22-μm filters to remove cells. Extracellular protein production by Escherichia coli Trans1, Comamonas testosteroni CNB1, Casimicrobium huifangae SJ-1, Rhodococcus erythropolis Q1R30-27, and Rhodococcus ruber H1Y01 was also determined in parallel. Strains of SJ-1, Q1R30-27, and H1Y01 were isolated from activated sludge of the Qinghe WWTP at the same time. All these bacteria were cultured in the liquid R2A medium under the same conditions, which are practicable to compare the production of extracellular proteins in our study.

16S rRNA gene sequencing and microbial community profiling.

The 170 samples (143 from activated sludge and 27 from foam) were used to profile microbial communities. One milliliter of each activated sludge or foam sample was centrifuged at 16,000 × g for 10 min at 4°C. Total DNAs were then extracted from 0.25 g (wet weight) of the resultant pellets with a PowerSoil DNA isolation kit (Qiagen, Inc., Germany) according to the manufacturer’s protocol. The purified DNAs were used as PCR templates for amplification of the hypervariable V4 region of 16S rRNA genes with the primer pair 515F (5′-GTGCCAGCMGCCGCGGTAA-3′) and 806R (5′-GGACTACHVGGGTWTCTAAT-3′). Amplicons were then sequenced on the Illumina HiSeq 2500 platform to generate 250-bp paired-end sequences. High-throughput sequencing data for V4 hypervariable 16S rRNA regions from other WWTP studies (4, 3638) were also retrieved. Representative sequences and the abundances of operational taxonomic units (OTUs) were generated using USEARCH v11.0 (45), as described previously (29). To identify the OTU that represents Ca. K. beijingensis and other known BFB (11), local BLAST (54) searches were conducted with cutoff values at ≥97% nucleotide similarity and at least coverage over 200 bp of the query 16S rRNA gene sequences.

Shotgun metagenomic sequencing and microbial community profiling.

Metagenomic DNA was extracted from 36 sludge samples and 12 foaming samples (see Data Set S2 in the supplemental material). Metagenomic libraries were prepared and sequenced on the Illumina HiSeq 2000 platform, producing 150-bp paired-end reads. Raw sequence data were first processed with the Illumina CASAVA pipeline. Reads were then trimmed based on quality scores using KneadDATA v0.7.4 (http://huttenhower.sph.harvard.edu/kneaddata) to generate clean reads. The clean reads were annotated using Kraken 2 and then used to estimate species-level abundances with Bracken v2.6 (https://github.com/jenniferlu717/Bracken/). A Kraken 2 (71) custom database was built based on the GTDB release 95 (72) using the Metagenomics Index Correction (https://github.com/rrwick/Metagenomics-Index-Correction) script. Briefly, GTDB-Tk was used to assign objective taxonomic classifications to Ca. K. beijingensis PM-38 in the GTDB database. The previous metagenomic bin genome of Kaistella sp002426785 exhibited 97.98% ANI similarity with Ca. K. beijingensis PM-38. To obtain more precise species-level abundances, the genome sequence of Ca. K. beijingensis PM-38 was added to the database, while the previously identified Kaistella sp002426785 was removed to refine the database.

Extraction and quantification of extracellular polymeric substances (EPS) from foam samples.

The 170 samples used for 16S rRNA gene sequencing were also extracted for EPS at the same time. Foam samples were first centrifuged at 4,000 × g for 10 min and subsequently lyophilized due to the unique physical properties of viscous foam. The lyophilized samples were weighed and resuspended in PBS buffer solution. EPS were then extracted by sonication at 25 W for 2 min (73), followed by harvesting with filtration through a 0.45-mm cellulose membrane and storage at −20°C (35). The protein, carbohydrate, and free fatty acid contents of EPS were then quantified. The protein and carbohydrate composition of extracted EPS were then analyzed as previously described (74). Carbohydrate contents were determined using the anthrone method with a glucose standard. Protein contents were measured with the Lowry method and bovine serum albumin (BSA) as the standard. Lastly, free fatty acids (FFAs) were extracted using a copper soap solvent and quantified using colorimetric methods with a palmitic acid standard (75).

Identification of Ca. K. beijingensis proteins from extracted extracellular polymeric substances.

The EPS extracted from activated sludge and foam samples (6 samples) collected in December 2018, when the Qinghe WWTP was suffering serious foaming problems, were used to analyze protein compositions and identify Ca. K. beijingensis proteins. Each extracted EPS sample was digested with trypsin overnight. Formic acid was then mixed with the digested samples, the pH was adjusted to 3.0, and the mixture was centrifuged at 12,000 × g for 5 min at room temperature. The supernatant was slowly loaded onto a C18 desalting column, washed with buffer (0.1% formic acid, 3% acetonitrile) three times, and then eluted with elution buffer (0.1% formic acid, 70% acetonitrile). The eluents from each sample were combined and lyophilized. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis was then conducted at Novogene Co., Ltd., Beijing, using the previously described method (76). All resulting spectra were searched against the predicted protein sequences of Ca. K. beijingensis PM-38 using the Proteome Discoverer 2.2. To improve the quality of analytical results, peptide spectrum matches (PSMs) with a credibility threshold of 99% were identified as PSMs. The final identified protein needed to contain at least one unique peptide and was retained when the false-discovery rate was <1.0%. Secreted proteins from Ca. K. beijingensis PM-38 that were significantly different between sludge and foam samples were investigated using the ALDEx2 (77) package for R.

Statistical and network analyses.

A Wilcoxon rank sum test was used to test for differences of median taxon abundances within metagenomic data between activated sludge and foam samples. In addition, a differential heat tree was visualized using the metacoder (78) package for R. A Kruskal-Wallis test was used to test for differences in multiple groups. To identify OTUs that were significantly enriched in foam samples across different seasons, the DESeq2 package was used to evaluate differences based on a model using a negative binomial distribution. The OTUs that consistently occurred in foam or activated sludge samples were selected to generate a network. Spearman’s correlations among OTUs were considered statistically robust when Spearman’s correlation coefficient (ρ) was >0.8, and P values <0.05 were adjusted using the method of Benjamini and Hochberg (79). The topology of the resulting network and greedy (80) modularity optimization were calculated using igraph (81) and visualized with Gephi (82). Other statistical analyses were conducted in R and visualized with ggplot2 (83) and ggstatsplot (https://CRAN.R-project.org/package=ggstatsplot).

Data availability.

Ca. K. beijingensis strain PM-38 is deposited in the China General Microbiological Culture Collection Center (CGMCC) under accession number CGMCC 1.30062 and the National Institute of Technology and Evaluation Biological Resource Center (NBRC) of Japan under accession number NBRC 114000. The complete genome sequences of PM-38 and Skermania piniformis DSM 43998T are available in the NCBI GenBank database under accession numbers CP071953 and CP079105, respectively. The shotgun metagenomic sequencing data for the Qinghe WWTP activated sludge samples are available in the NCBI SRA database under accession number PRJNA718193. The 16S rRNA gene amplicon sequencing data are available under the SRA accession numbers PRJNA550218 and PRJNA717150. The representative sequences of OTUs from the global activated sludge bacterial data set are available on the website http://gwmc.ou.edu/data-disclose.html. In addition, the full-length 16S rRNA gene sequence of Ca. K. beijingensis was obtained from the complete genome via the use of RNAmmer (84) and was deposited in GenBank under the accession number MT804638.

ACKNOWLEDGMENT

This study was supported by Construction and Application of Activated Sludge Artificial Multicellular System Project from the Ministry of Science and Technology of China (2019YFA0905500).

Supplemental Material

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Information & Contributors

Information

Published In

cover image Applied and Environmental Microbiology
Applied and Environmental Microbiology
Volume 87Number 2424 November 2021
eLocator: e01534-21
Editor: Nicole R. Buan, University of Nebraska-Lincoln
PubMed: 34586909

History

Received: 30 July 2021
Accepted: 20 September 2021
Accepted manuscript posted online: 29 September 2021
Published online: 24 November 2021

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Keywords

  1. wastewater treatment plant (WWTP)
  2. biological foaming
  3. Candidatus Kaistella beijingensis
  4. bulking and foaming bacteria (BFB)
  5. activated sludge
  6. Kaistella beijingensis

Contributors

Authors

Yang Song
State Key Laboratory of Microbial Resources and Environmental Microbiology Research Center at Institute of Microbiology, Chinese Academy of Sciences, Beijing, China
CAS Key Laboratory of Environmental Biotechnology and RCEES-IMCAS-UCAS Joint Laboratory for Environmental Microbial Technology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, China
University of Chinese Academy of Sciences, Beijing, China
The Ecology and Environment Branch of State Center for Research and Development of Oil Shale Exploitation, PetroChina Planning and Engineering Institute, Beijing, China
Cheng-Ying Jiang
State Key Laboratory of Microbial Resources and Environmental Microbiology Research Center at Institute of Microbiology, Chinese Academy of Sciences, Beijing, China
CAS Key Laboratory of Environmental Biotechnology and RCEES-IMCAS-UCAS Joint Laboratory for Environmental Microbial Technology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, China
University of Chinese Academy of Sciences, Beijing, China
Zong-Lin Liang
State Key Laboratory of Microbial Resources and Environmental Microbiology Research Center at Institute of Microbiology, Chinese Academy of Sciences, Beijing, China
University of Chinese Academy of Sciences, Beijing, China
Hai-Zhen Zhu
State Key Laboratory of Microbial Resources and Environmental Microbiology Research Center at Institute of Microbiology, Chinese Academy of Sciences, Beijing, China
University of Chinese Academy of Sciences, Beijing, China
Yong Jiang
Beijing Drainage Group Co., Ltd, Beijing, China
Ye Yin
BGI-Qingdao, Qingdao, China
Ya-Ling Qin
State Key Laboratory of Microbial Resources and Environmental Microbiology Research Center at Institute of Microbiology, Chinese Academy of Sciences, Beijing, China
University of Chinese Academy of Sciences, Beijing, China
Hao-Jie Huang
State Key Laboratory of Microbial Resources and Environmental Microbiology Research Center at Institute of Microbiology, Chinese Academy of Sciences, Beijing, China
State Key Laboratory of Microbial Biotechnology, Shandong University, Qingdao, China
Bao-Jun Wang
State Key Laboratory of Microbial Resources and Environmental Microbiology Research Center at Institute of Microbiology, Chinese Academy of Sciences, Beijing, China
Zi-Yan Wei
State Key Laboratory of Microbial Resources and Environmental Microbiology Research Center at Institute of Microbiology, Chinese Academy of Sciences, Beijing, China
Rui-Xue Cheng
State Key Laboratory of Microbial Resources and Environmental Microbiology Research Center at Institute of Microbiology, Chinese Academy of Sciences, Beijing, China
Zhi-Pei Liu
State Key Laboratory of Microbial Resources and Environmental Microbiology Research Center at Institute of Microbiology, Chinese Academy of Sciences, Beijing, China
University of Chinese Academy of Sciences, Beijing, China
Yao Liu
Beijing Drainage Group Co., Ltd, Beijing, China
Tao Jin
BGI-Qingdao, Qingdao, China
Ai-Jie Wang [email protected]
CAS Key Laboratory of Environmental Biotechnology and RCEES-IMCAS-UCAS Joint Laboratory for Environmental Microbial Technology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, China
University of Chinese Academy of Sciences, Beijing, China
State Key Laboratory of Microbial Resources and Environmental Microbiology Research Center at Institute of Microbiology, Chinese Academy of Sciences, Beijing, China
State Key Laboratory of Microbial Biotechnology, Shandong University, Qingdao, China
University of Chinese Academy of Sciences, Beijing, China

Editor

Nicole R. Buan
Editor
University of Nebraska-Lincoln

Notes

Yang Song and Cheng-Ying Jiang contributed equally to this work. Author order was determined in order of increasing seniority.

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